Mental Illness

Mental Illness

Load and clean data

mental_df = 
  read_csv("./data/mental_data.csv") %>% 
  janitor::clean_names() %>% 
  mutate(
    any_mental_num = any_mental_num / 1000000,
    any_mental_per = any_mental_per * 100,
    ser_mental_num = ser_mental_num / 1000000,
    ser_mental_per = ser_mental_per * 100,
    state_abb = state.abb[match(state, state.name)],
    region = state.region[match(state, state.name)]
  ) %>% 
  mutate(
    state_abb = replace(state_abb, state == "District of Columbia", "DC"))

Map: Percent of adults reporting any mental illness by state between 2019-2020

state_mental=
  plot_usmap(
    data = mental_df,
    regions = "state",
    values = "any_mental_per", 
    labels = TRUE, label_color = "white") +
  labs(
    title = "Percent of adults reporting any mental illness for each state, 2019-2022"
  ) +
  scale_fill_continuous(
    name = "Mental illness percent (%)",
    label = scales::comma) +
  theme(legend.position = "right")

ggplotly(state_mental)

According to the mental health data collected between 2019 -2020, the mental illness percents are high in the US overal, with variations between states.

Any/Serious Mental illness numbers (million), by region, 2019-2020

any_mental_plot = 
  mental_df %>% 
    group_by(region) %>%
    drop_na() %>% 
    summarize(any_mental_num = sum(any_mental_num)) %>% 
    ggplot(
      aes(x = region, y = any_mental_num, fill = region)) +
    geom_bar(stat = "identity") +
    labs(
      title = "Any Mental Illness Number, by Region, 2019-2020",
      x = "Region",
      y = "Mental illness number (million)",
      fill = "Region") +
  theme(legend.position = "bottom")

ser_mental_plot =
  mental_df %>% 
    group_by(region) %>%
    drop_na() %>% 
    summarize(ser_mental_num = sum(ser_mental_num)) %>% 
    ggplot(
      aes(x = region, y = ser_mental_num, fill = region)) +
    geom_bar(stat = "identity") +
    labs(
      title = "Serious Mental Illness Number, by Region, 2019-2020",
      x = "Region",
      y = "Mental illness number (million)",
      fill = "Region") +
    theme(legend.position = "bottom")

grid.arrange(any_mental_plot, ser_mental_plot, ncol =2) 

Comment Both any mental illness and serious mental illness are highest in the South, lowest in the northeast.

Any/Serious Mental illness percent, Top 10 states, 2019-2020

any_top10_plot =
  mental_df %>% 
    filter(row_number(desc(any_mental_per)) <= 10) %>% 
    mutate(
      state = fct_reorder(state, any_mental_per)
    ) %>% 
    ggplot(
      aes(x = any_mental_per, y = state, fill = state)) +
      geom_bar(stat = "identity") +
      labs(
        title = "Any Mental Illness Percent, Top 10 States",
        x = "Any Mental illness percent (%)",
        y = "State",
        fill = "State") +
    theme(legend.position = "bottom")

ser_top10_plot =
  mental_df %>% 
    filter(row_number(desc(ser_mental_per)) <= 10) %>% 
    mutate(
      state = fct_reorder(state, ser_mental_per)
    ) %>% 
    ggplot(
      aes(x = ser_mental_per, y = state, fill = state)) +
      geom_bar(stat = "identity") +
      labs(
        title = "Serious Mental Illness Percent, Top 10 States",
        x = "Serious mental illness percent (%)",
        y = "State",
      fill = "State") +
    theme(legend.position = "bottom")

grid.arrange(any_top10_plot, ser_top10_plot, ncol =2) 

Comment

The top 10 states for any and serious mental illness are 8/10 the same, except Washington, Rhode Island, Arkansas and Indiana. Ultah has the highest any/serious mental illness percent.